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Efficient Drone Hijacking Detection using Onboard Motion Sensors
Northeastern Univ, Shenyang, Peoples R China..
Hong Kong Polytech Univ, Hong Kong, Hong Kong, Peoples R China..
Northeastern Univ, Shenyang, Peoples R China..
Chongqing Univ, Chongqing, Peoples R China..
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2017 (English)In: Proceedings Of The 2017 Design, Automation & Test In Europe Conference & Exhibition (DATE), IEEE , 2017, p. 1414-1419Conference paper, Published paper (Refereed)
Abstract [en]

The fast growth of civil drones raises significant security challenges. A legitimate drone may be hijacked by GPS spoofing for illegal activities, such as terrorist attacks. The target of this paper is to develop techniques to let drones detect whether they have been hijacked using onboard motion sensors (accelerometers and gyroscopes). Ideally, the linear acceleration and angular velocity measured by motion sensors can be used to estimate the position of a drone, which can be compared with the position reported by GPS to detect whether the drone has been hijacked. However, the position estimation by motion sensors is very inaccurate due to the significant error accumulation over time. In this paper, we propose a novel method to detect hijacking based on motion sensors measurements and GPS, which overcomes the accumulative error problem. The computational complexity of our method is very low, and thus is suitable to be implemented in the micro-controllers of drones. Experiments with a quad-rotor drone are conducted to show the effectiveness of the proposed method.

Place, publisher, year, edition, pages
IEEE , 2017. p. 1414-1419
Series
Design Automation and Test in Europe Conference and Exhibition, ISSN 1530-1591
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:uu:diva-335836DOI: 10.23919/DATE.2017.7927214ISI: 000404171500266ISBN: 978-3-9815370-9-3 (electronic)OAI: oai:DiVA.org:uu-335836DiVA, id: diva2:1166540
Conference
20th Conference and Exhibition on Design, Automation and Test in Europe (DATE), MAR 27-31, 2017, EPFL Campus, Lausanne, SWITZERLAND
Available from: 2017-12-15 Created: 2017-12-15 Last updated: 2018-01-13Bibliographically approved

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Wang, Yi

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